Continual Adaptation of Multitask World Model with Embedding Retrieval

Recent advances in world models have enabled generalist agents to learn diverse continuous control tasks within a single model. However, joint training on all desired tasks is not always feasible, as limited resources constrain access to collected data and simulation environments at a given time. An important goal is therefore to adapt pretrained multitask agents to newly accessible tasks while retaining their existing capabilities. We propose MARR, a framework for multitask world model adaptation with capability retention. Given a short probing trajectory, our framework retrieves a task-relevant dynamics representation from the pretrained world model to initialize adaptation, enabling the agent to learn multiple new tasks efficiently. We adapt world-model weights through a factorized parameterization that learns new combinations of fixed pretrained bases, while exemplar-based distillation helps retain prior knowledge and skills. Experiments across diverse continuous control tasks show that our approach learns new tasks while retaining performance on previously learned tasks and outperforms a range of existing baselines. These results support multitask adaptation as a promising approach to broadening the capabilities of a multitask model.